package com.leon.datalink.core.persistence;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.util.*;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.atomic.AtomicLong;

/**
 * DataLink Actor持久化性能优化工具
 * 
 * 提供自动化的性能分析、调优建议和优化实施功能：
 * 1. 性能瓶颈自动检测
 * 2. 配置参数自动调优
 * 3. 存储优化建议
 * 4. 内存使用优化
 * 5. 快照策略优化
 * 6. 批处理参数调优
 * 7. 性能报告生成
 * 8. 自动化优化实施
 * 
 * @author leon
 */
public class PerformanceOptimizationTool {
    
    private static final Logger logger = LoggerFactory.getLogger(PerformanceOptimizationTool.class);
    
    private PersistenceConfigManager configManager;
    private PersistenceMetricsManager metricsManager;
    private SnapshotStrategyManager snapshotManager;
    
    // 性能阈值配置
    private static final double HIGH_LATENCY_THRESHOLD = 1000.0; // 1ms
    private static final double LOW_THROUGHPUT_THRESHOLD = 1000.0; // 1000 ops/s
    private static final double HIGH_MEMORY_USAGE_THRESHOLD = 0.8; // 80%
    private static final double HIGH_ERROR_RATE_THRESHOLD = 0.01; // 1%
    
    // 优化建议缓存
    private final Map<String, OptimizationRecommendation> recommendations = new ConcurrentHashMap<>();
    private final AtomicLong lastOptimizationTime = new AtomicLong(0);
    
    public PerformanceOptimizationTool() {
        this.configManager = PersistenceConfigManager.getInstance();
        this.metricsManager = PersistenceMetricsManager.getInstance();
        this.snapshotManager = SnapshotStrategyManager.getInstance();

        logger.info("性能优化工具初始化完成");
    }
    
    /**
     * 执行全面的性能分析
     */
    public PerformanceAnalysisResult analyzePerformance() {
        logger.info("开始执行性能分析...");
        
        PerformanceAnalysisResult result = new PerformanceAnalysisResult();
        
        // 1. 延迟分析
        analyzeLatency(result);
        
        // 2. 吞吐量分析
        analyzeThroughput(result);
        
        // 3. 内存使用分析
        analyzeMemoryUsage(result);
        
        // 4. 错误率分析
        analyzeErrorRate(result);
        
        // 5. 存储性能分析
        analyzeStoragePerformance(result);
        
        // 6. 快照策略分析
        analyzeSnapshotStrategy(result);
        
        logger.info("性能分析完成，发现 {} 个性能问题", result.getIssueCount());
        return result;
    }
    
    /**
     * 生成优化建议
     */
    public List<OptimizationRecommendation> generateRecommendations() {
        logger.info("生成优化建议...");
        
        PerformanceAnalysisResult analysis = analyzePerformance();
        List<OptimizationRecommendation> recommendations = new ArrayList<>();
        
        // 基于分析结果生成建议
        if (analysis.hasHighLatency()) {
            recommendations.add(createLatencyOptimizationRecommendation(analysis));
        }
        
        if (analysis.hasLowThroughput()) {
            recommendations.add(createThroughputOptimizationRecommendation(analysis));
        }
        
        if (analysis.hasHighMemoryUsage()) {
            recommendations.add(createMemoryOptimizationRecommendation(analysis));
        }
        
        if (analysis.hasHighErrorRate()) {
            recommendations.add(createErrorRateOptimizationRecommendation(analysis));
        }
        
        if (analysis.hasStorageIssues()) {
            recommendations.add(createStorageOptimizationRecommendation(analysis));
        }
        
        if (analysis.hasSnapshotIssues()) {
            recommendations.add(createSnapshotOptimizationRecommendation(analysis));
        }
        
        // 缓存建议
        for (OptimizationRecommendation rec : recommendations) {
            this.recommendations.put(rec.getId(), rec);
        }
        
        logger.info("生成了 {} 个优化建议", recommendations.size());
        return recommendations;
    }
    
    /**
     * 自动应用优化建议
     */
    public OptimizationResult applyOptimizations(List<String> recommendationIds) {
        logger.info("开始应用优化建议...");
        
        OptimizationResult result = new OptimizationResult();
        
        for (String id : recommendationIds) {
            OptimizationRecommendation rec = recommendations.get(id);
            if (rec != null) {
                try {
                    applyRecommendation(rec);
                    result.addSuccess(rec);
                    logger.info("成功应用优化建议: {}", rec.getTitle());
                } catch (Exception e) {
                    result.addFailure(rec, e);
                    logger.error("应用优化建议失败: {}", rec.getTitle(), e);
                }
            }
        }
        
        lastOptimizationTime.set(System.currentTimeMillis());
        logger.info("优化应用完成，成功: {}, 失败: {}", 
                   result.getSuccessCount(), result.getFailureCount());
        
        return result;
    }
    
    /**
     * 生成性能优化报告
     */
    public String generateOptimizationReport() {
        StringBuilder report = new StringBuilder();
        report.append("=== DataLink Actor持久化性能优化报告 ===\n");
        report.append("生成时间: ").append(new Date()).append("\n\n");
        
        // 当前性能状态
        PerformanceAnalysisResult analysis = analyzePerformance();
        report.append("## 当前性能状态\n");
        report.append(analysis.getSummary()).append("\n\n");
        
        // 优化建议
        List<OptimizationRecommendation> recommendations = generateRecommendations();
        report.append("## 优化建议 (").append(recommendations.size()).append("个)\n");
        for (OptimizationRecommendation rec : recommendations) {
            report.append("### ").append(rec.getTitle()).append("\n");
            report.append("优先级: ").append(rec.getPriority()).append("\n");
            report.append("描述: ").append(rec.getDescription()).append("\n");
            report.append("预期收益: ").append(rec.getExpectedBenefit()).append("\n\n");
        }
        
        // 历史优化记录
        report.append("## 历史优化记录\n");
        if (lastOptimizationTime.get() > 0) {
            report.append("最后优化时间: ").append(new Date(lastOptimizationTime.get())).append("\n");
        } else {
            report.append("尚未执行过优化\n");
        }
        
        return report.toString();
    }
    
    // 私有方法实现
    
    private void analyzeLatency(PerformanceAnalysisResult result) {
        try {
            // 真实延迟分析
            double avgLatency = metricsManager.getAverageLatency();
            double p95Latency = metricsManager.getP95Latency();
            double p99Latency = metricsManager.getP99Latency();

            if (avgLatency > HIGH_LATENCY_THRESHOLD) {
                result.addIssue("高平均延迟",
                    String.format("平均延迟 %.2fms 超过阈值 %.2fms", avgLatency, HIGH_LATENCY_THRESHOLD));
                result.addRecommendation("优化数据访问路径，考虑增加缓存层");
            }

            if (p95Latency > HIGH_LATENCY_THRESHOLD * 2) {
                result.addIssue("高P95延迟",
                    String.format("P95延迟 %.2fms 过高", p95Latency));
                result.addRecommendation("检查慢查询，优化数据库索引");
            }

            if (p99Latency > HIGH_LATENCY_THRESHOLD * 3) {
                result.addIssue("高P99延迟",
                    String.format("P99延迟 %.2fms 过高，可能存在性能瓶颈", p99Latency));
                result.addRecommendation("分析长尾延迟，考虑异步处理");
            }

        } catch (Exception e) {
            logger.error("延迟分析失败", e);
            result.addIssue("延迟分析错误", "无法获取延迟指标: " + e.getMessage());
        }
    }
    
    private void analyzeThroughput(PerformanceAnalysisResult result) {
        try {
            // 真实吞吐量分析
            double currentThroughput = metricsManager.getThroughput();
            double peakThroughput = metricsManager.getPeakThroughput();
            double avgThroughput = metricsManager.getAverageThroughput();

            if (currentThroughput < LOW_THROUGHPUT_THRESHOLD) {
                result.addIssue("低吞吐量",
                    String.format("当前吞吐量 %.0f ops/s 低于预期阈值 %.0f ops/s",
                        currentThroughput, LOW_THROUGHPUT_THRESHOLD));
                result.addRecommendation("检查系统瓶颈，考虑水平扩展或优化处理逻辑");
            }

            // 检查吞吐量波动
            if (peakThroughput > 0 && currentThroughput < peakThroughput * 0.5) {
                result.addIssue("吞吐量波动",
                    String.format("当前吞吐量 %.0f ops/s 相比峰值 %.0f ops/s 下降超过50%%",
                        currentThroughput, peakThroughput));
                result.addRecommendation("分析吞吐量下降原因，检查资源使用情况");
            }

            // 检查平均吞吐量趋势
            if (avgThroughput > 0 && currentThroughput < avgThroughput * 0.8) {
                result.addIssue("吞吐量趋势",
                    String.format("当前吞吐量 %.0f ops/s 低于平均值 %.0f ops/s",
                        currentThroughput, avgThroughput));
                result.addRecommendation("监控系统负载，考虑性能调优");
            }

        } catch (Exception e) {
            logger.error("吞吐量分析失败", e);
            result.addIssue("吞吐量分析错误", "无法获取吞吐量指标: " + e.getMessage());
        }
    }
    
    private void analyzeMemoryUsage(PerformanceAnalysisResult result) {
        // 模拟内存使用分析
        double memoryUsage = getMemoryUsageRatio();
        if (memoryUsage > HIGH_MEMORY_USAGE_THRESHOLD) {
            result.addIssue("高内存使用", "内存使用率 " + (memoryUsage * 100) + "% 过高");
        }
    }
    
    private void analyzeErrorRate(PerformanceAnalysisResult result) {
        // 模拟错误率分析
        double errorRate = metricsManager.getErrorRate();
        if (errorRate > HIGH_ERROR_RATE_THRESHOLD) {
            result.addIssue("高错误率", "错误率 " + (errorRate * 100) + "% 超过阈值");
        }
    }
    
    private void analyzeStoragePerformance(PerformanceAnalysisResult result) {
        // 模拟存储性能分析
        double storageLatency = getStorageLatency();
        if (storageLatency > 100.0) { // 100ms
            result.addIssue("存储性能", "存储延迟 " + storageLatency + "ms 过高");
        }
    }
    
    private void analyzeSnapshotStrategy(PerformanceAnalysisResult result) {
        // 模拟快照策略分析
        int snapshotFrequency = (int) snapshotManager.getAverageSnapshotFrequency();
        if (snapshotFrequency < 100) {
            result.addIssue("快照频率", "快照频率过高，影响性能");
        }
    }
    
    private OptimizationRecommendation createLatencyOptimizationRecommendation(PerformanceAnalysisResult analysis) {
        return new OptimizationRecommendation(
            "latency-opt-001",
            "延迟优化",
            "通过调整批处理大小和异步处理来降低延迟",
            OptimizationPriority.HIGH,
            "预期降低延迟30-50%"
        );
    }
    
    private OptimizationRecommendation createThroughputOptimizationRecommendation(PerformanceAnalysisResult analysis) {
        return new OptimizationRecommendation(
            "throughput-opt-001",
            "吞吐量优化",
            "增加并发处理线程和优化批处理策略",
            OptimizationPriority.HIGH,
            "预期提升吞吐量50-100%"
        );
    }
    
    private OptimizationRecommendation createMemoryOptimizationRecommendation(PerformanceAnalysisResult analysis) {
        return new OptimizationRecommendation(
            "memory-opt-001",
            "内存优化",
            "调整缓存大小和垃圾回收策略",
            OptimizationPriority.MEDIUM,
            "预期降低内存使用20-30%"
        );
    }
    
    private OptimizationRecommendation createErrorRateOptimizationRecommendation(PerformanceAnalysisResult analysis) {
        return new OptimizationRecommendation(
            "error-opt-001",
            "错误率优化",
            "增强错误处理和重试机制",
            OptimizationPriority.HIGH,
            "预期降低错误率80%以上"
        );
    }
    
    private OptimizationRecommendation createStorageOptimizationRecommendation(PerformanceAnalysisResult analysis) {
        return new OptimizationRecommendation(
            "storage-opt-001",
            "存储优化",
            "优化存储配置和I/O策略",
            OptimizationPriority.MEDIUM,
            "预期提升存储性能30-50%"
        );
    }
    
    private OptimizationRecommendation createSnapshotOptimizationRecommendation(PerformanceAnalysisResult analysis) {
        return new OptimizationRecommendation(
            "snapshot-opt-001",
            "快照策略优化",
            "调整快照频率和压缩策略",
            OptimizationPriority.LOW,
            "预期降低存储开销20-40%"
        );
    }
    
    private void applyRecommendation(OptimizationRecommendation rec) {
        // 根据建议类型应用具体的优化
        switch (rec.getId()) {
            case "latency-opt-001":
                // 应用延迟优化
                configManager.setBatchSize(200);
                configManager.setAsyncProcessing(true);
                break;
            case "throughput-opt-001":
                // 应用吞吐量优化
                configManager.setConcurrentThreads(10);
                configManager.setBatchTimeout(50);
                break;
            case "memory-opt-001":
                // 应用内存优化
                configManager.setCacheSize(500);
                configManager.setGcStrategy("G1GC");
                break;
            // 其他优化...
        }
    }
    
    private double getMemoryUsageRatio() {
        Runtime runtime = Runtime.getRuntime();
        long totalMemory = runtime.totalMemory();
        long freeMemory = runtime.freeMemory();
        return (double) (totalMemory - freeMemory) / totalMemory;
    }
    
    private double getStorageLatency() {
        try {
            // 真实存储延迟测试
            long startTime = System.nanoTime();

            // 执行一个简单的存储操作来测试延迟
            // 这里使用文件系统操作作为存储延迟的代理指标
            java.io.File tempFile = java.io.File.createTempFile("storage_test", ".tmp");
            try (java.io.FileWriter writer = new java.io.FileWriter(tempFile)) {
                writer.write("storage latency test");
                writer.flush();
            }

            // 读取文件
            try (java.io.FileReader reader = new java.io.FileReader(tempFile)) {
                char[] buffer = new char[1024];
                reader.read(buffer);
            }

            // 删除临时文件
            tempFile.delete();

            long endTime = System.nanoTime();
            double latencyMs = (endTime - startTime) / 1_000_000.0;

            return latencyMs;

        } catch (Exception e) {
            logger.error("存储延迟测试失败", e);
            // 返回一个保守的估计值
            return 50.0; // 50ms
        }
    }
    
    // 内部类定义
    
    public static class PerformanceAnalysisResult {
        private final List<String> issues = new ArrayList<>();
        private final List<String> recommendations = new ArrayList<>();
        private final Map<String, Object> metrics = new HashMap<>();

        public void addIssue(String type, String description) {
            issues.add(type + ": " + description);
        }

        public void addRecommendation(String recommendation) {
            recommendations.add(recommendation);
        }
        
        public int getIssueCount() {
            return issues.size();
        }
        
        public boolean hasHighLatency() {
            return issues.stream().anyMatch(issue -> issue.contains("高延迟"));
        }
        
        public boolean hasLowThroughput() {
            return issues.stream().anyMatch(issue -> issue.contains("低吞吐量"));
        }
        
        public boolean hasHighMemoryUsage() {
            return issues.stream().anyMatch(issue -> issue.contains("高内存使用"));
        }
        
        public boolean hasHighErrorRate() {
            return issues.stream().anyMatch(issue -> issue.contains("高错误率"));
        }
        
        public boolean hasStorageIssues() {
            return issues.stream().anyMatch(issue -> issue.contains("存储性能"));
        }
        
        public boolean hasSnapshotIssues() {
            return issues.stream().anyMatch(issue -> issue.contains("快照频率"));
        }
        
        public String getSummary() {
            if (issues.isEmpty()) {
                return "性能状态良好，未发现问题";
            }
            return "发现 " + issues.size() + " 个性能问题:\n" + String.join("\n", issues);
        }
    }
    
    public static class OptimizationRecommendation {
        private final String id;
        private final String title;
        private final String description;
        private final OptimizationPriority priority;
        private final String expectedBenefit;
        
        public OptimizationRecommendation(String id, String title, String description, 
                                        OptimizationPriority priority, String expectedBenefit) {
            this.id = id;
            this.title = title;
            this.description = description;
            this.priority = priority;
            this.expectedBenefit = expectedBenefit;
        }
        
        // Getters
        public String getId() { return id; }
        public String getTitle() { return title; }
        public String getDescription() { return description; }
        public OptimizationPriority getPriority() { return priority; }
        public String getExpectedBenefit() { return expectedBenefit; }
    }
    
    public enum OptimizationPriority {
        HIGH, MEDIUM, LOW
    }
    
    public static class OptimizationResult {
        private final List<OptimizationRecommendation> successes = new ArrayList<>();
        private final Map<OptimizationRecommendation, Exception> failures = new HashMap<>();
        
        public void addSuccess(OptimizationRecommendation rec) {
            successes.add(rec);
        }
        
        public void addFailure(OptimizationRecommendation rec, Exception e) {
            failures.put(rec, e);
        }
        
        public int getSuccessCount() { return successes.size(); }
        public int getFailureCount() { return failures.size(); }
    }
    
    public static void main(String[] args) {
        logger.info("=== DataLink Actor持久化性能优化工具测试 ===");
        
        try {
            PerformanceOptimizationTool tool = new PerformanceOptimizationTool();
            
            // 1. 性能分析
            PerformanceAnalysisResult analysis = tool.analyzePerformance();
            logger.info("性能分析完成，发现 {} 个问题", analysis.getIssueCount());
            
            // 2. 生成优化建议
            List<OptimizationRecommendation> recommendations = tool.generateRecommendations();
            logger.info("生成了 {} 个优化建议", recommendations.size());
            
            // 3. 生成优化报告
            String report = tool.generateOptimizationReport();
            logger.info("优化报告:\n{}", report);
            
            logger.info("=== 性能优化工具测试完成 ===");
            
        } catch (Exception e) {
            logger.error("性能优化工具测试失败", e);
        }
    }
}
